The computational prediction of novel drug-target interactions (DTIs) may hasten drug repositioning while reducing costs. Most previous methods integrated multiple kinds of connections between drugs and targets by constructing shallow models. These methods failed to learn low-dimension feature vectors for drugs and targets and ignored their distribution. We proposed a graph convolutional network and generative adversarial network-based method, GANDTI, to predict drug-target interactions. We constructed a drug-target heterogeneous network to integrate various connections between drugs and targets. A graph convolutional autoencoder was established to learn the network embedding of the drug and target nodes in a low-dimensional feature space. This encoder deeply integrated the various connections and attributes of drug and target nodes. A generative adversarial network was introduced to regularize the feature vectors into a Gaussian distribution. Severe class imbalance exists between known and unknown DTIs. We built a classifier based on the ensemble learning model, LightGBM, to evaluate the interaction propensities of drugs and targets. This classifier completely exploited all unknown DTIs and counteracted the negative effects of class imbalance. The experimental results indicated that GANDTI performs better than several state-of-the-art methods. Additionally, case studies of five drugs demonstrated the ability of GANDTI to discover potential drugs' targets.

Identifying novel indications for approved drugs can accelerate drug development and reduce research costs. Most previous studies used shallow models for prioritizing the potential drug-related diseases and failed to deeply integrate the paths between drugs and diseases which may contain additional association information. A deep-learning-based method for predicting drug–disease associations by integrating useful information is needed. We proposed a novel method based on a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM)—CBPred—for predicting drug-related diseases. Our method deeply integrates similarities and associations between drugs and diseases, and paths among drug-disease pairs. The CNN-based framework focuses on learning the original representation of a drug-disease pair from their similarities and associations. As the drug-disease association possibility also depends on the multiple paths between them, the BiLSTM-based framework mainly learns the path representation of the drug-disease pair. In addition, considering that different paths have discriminate contributions to the association prediction, an attention mechanism at path level is constructed. Our method, CBPred, showed better performance and retrieved more real associations in the front of the results, which is more important for biologists. Case studies further confirmed that CBPred can discover potential drug-disease associations.

Predicting novel uses for drugs using their chemical, pharmacological, and indication information contributes to minimizing costs and development periods. Most previous prediction methods focused on integrating the similarity and association information of drugs and diseases. However, they tended to construct shallow prediction models to predict drug-associated diseases, which make deeply integrating the information difficult. Further, path information between drugs and diseases is important auxiliary information for association prediction, while it is not deeply integrated. We present a deep learning-based method, CGARDP, for predicting drug-related candidate disease indications. CGARDP establishes a feature matrix by exploiting a variety of biological premises related to drugs and diseases. A novel model based on convolutional neural network (CNN) and gated recurrent unit (GRU) is constructed to learn the local and path representations for a drug-disease pair. The CNN-based framework on the left of the model learns the local representation of the drug-disease pair from their feature matrix. As the different paths have discriminative contributions to the drug-disease association prediction, we construct an attention mechanism at the path level to learn the informative paths. In the right part, a GRU-based framework learns the path representation based on path information between the drug and the disease. Cross-validation results indicate that CGARDP performs better than several state-of-the-art methods. Further, CGARDP retrieves more real drug-disease associations in the top part of the prediction result that are of concern to biologists. Case studies on five drugs demonstrate that CGARDP can discover potential drug-related disease indications.